/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    KDConditionalEstimator.java
 *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.estimators;

import weka.core.RevisionUtils;

/**
 * Conditional probability estimator for a numeric domain conditional upon a
 * discrete domain (utilises separate kernel estimators for each discrete
 * conditioning value).
 * 
 * @author Len Trigg (trigg@cs.waikato.ac.nz)
 * @version $Revision: 1.8 $
 */
public class KDConditionalEstimator implements ConditionalEstimator {

	/** Hold the sub-estimators */
	private KernelEstimator[] m_Estimators;

	/**
	 * Constructor
	 * 
	 * @param numCondSymbols
	 *            the number of conditioning symbols
	 * @param precision
	 *            the precision to which numeric values are given. For example,
	 *            if the precision is stated to be 0.1, the values in the
	 *            interval (0.25,0.35] are all treated as 0.3.
	 */
	public KDConditionalEstimator(int numCondSymbols, double precision) {

		m_Estimators = new KernelEstimator[numCondSymbols];
		for (int i = 0; i < numCondSymbols; i++) {
			m_Estimators[i] = new KernelEstimator(precision);
		}
	}

	/**
	 * Add a new data value to the current estimator.
	 * 
	 * @param data
	 *            the new data value
	 * @param given
	 *            the new value that data is conditional upon
	 * @param weight
	 *            the weight assigned to the data value
	 */
	public void addValue(double data, double given, double weight) {

		m_Estimators[(int) given].addValue(data, weight);
	}

	/**
	 * Get a probability estimator for a value
	 * 
	 * @param given
	 *            the new value that data is conditional upon
	 * @return the estimator for the supplied value given the condition
	 */
	public Estimator getEstimator(double given) {

		return m_Estimators[(int) given];
	}

	/**
	 * Get a probability estimate for a value
	 * 
	 * @param data
	 *            the value to estimate the probability of
	 * @param given
	 *            the new value that data is conditional upon
	 * @return the estimated probability of the supplied value
	 */
	public double getProbability(double data, double given) {

		return getEstimator(given).getProbability(data);
	}

	/** Display a representation of this estimator */
	public String toString() {

		String result = "KD Conditional Estimator. " + m_Estimators.length
				+ " sub-estimators:\n";
		for (int i = 0; i < m_Estimators.length; i++) {
			result += "Sub-estimator " + i + ": " + m_Estimators[i];
		}
		return result;
	}

	/**
	 * Returns the revision string.
	 * 
	 * @return the revision
	 */
	public String getRevision() {
		return RevisionUtils.extract("$Revision: 1.8 $");
	}

	/**
	 * Main method for testing this class.
	 * 
	 * @param argv
	 *            should contain a sequence of pairs of integers which will be
	 *            treated as numeric, symbolic.
	 */
	public static void main(String[] argv) {

		try {
			if (argv.length == 0) {
				System.out.println("Please specify a set of instances.");
				return;
			}
			int currentA = Integer.parseInt(argv[0]);
			int maxA = currentA;
			int currentB = Integer.parseInt(argv[1]);
			int maxB = currentB;
			for (int i = 2; i < argv.length - 1; i += 2) {
				currentA = Integer.parseInt(argv[i]);
				currentB = Integer.parseInt(argv[i + 1]);
				if (currentA > maxA) {
					maxA = currentA;
				}
				if (currentB > maxB) {
					maxB = currentB;
				}
			}
			KDConditionalEstimator newEst = new KDConditionalEstimator(
					maxB + 1, 1);
			for (int i = 0; i < argv.length - 1; i += 2) {
				currentA = Integer.parseInt(argv[i]);
				currentB = Integer.parseInt(argv[i + 1]);
				System.out.println(newEst);
				System.out.println("Prediction for " + currentA + '|'
						+ currentB + " = "
						+ newEst.getProbability(currentA, currentB));
				newEst.addValue(currentA, currentB, 1);
			}
		} catch (Exception e) {
			System.out.println(e.getMessage());
		}
	}
}
